Survey on the benefits of using memristors for PUFs

Aljafar, Muayad J.; Acken, John M.

doi:10.1080/17445760.2021.1972295

Cited by 2 publications

(2 citation statements)

References 89 publications

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“…The authors [82] proposed the PUF to overcome the limitations of current security strategies, TiOx/Al2O3-based memristors are used to fabricate a 32x32 crossbar array, and its electrical properties, including its set voltage distribution, are examined. The authors [83] reviewed memristive PUFs (Physical Unclonable Functions) mentioned in the literature and defined the inducement for the usage of memristor technology for enforcing PUFs. they focused on PUFs' applications, sizes, analysis, and physical variations.…”

Section: Implementation Of Memristive Puf (M-puf)mentioning

confidence: 99%

Memristive Physical Unclonable Functions: The State-of-the-Art Technology

Al-Khaboori,

Al-Mashhadani

2023

IJSSE

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Internet of Things connected many useful electronic devices to each other through the internet, and sharing private and sensitive data between these devices needs secure access and communication. One of the best solutions for this purpose is hardware security primitives such as Physically Unclonable Functions (PUFs). PUFs are cryptographic primitives that are employed to produce a unique and reliable digital fingerprint for a particular electronic circuit. This digital fingerprint is used in many security applications such as chip identification, authentication, and secret key storage and generation. The emergence of memristors (Memory-Resistor) as new nanotechnologies are utilized extensively in hardware security applications such as Memristive PUFs. Research progress in Memristive PUFs resulted in improved performance metrics of PUFs due to memristors' unique characteristics. This article provides an investigation of different design approaches of Memristive PUFs that were introduced in the literature. Then, provide detailed performance evaluation results obtained by simulation and fabrication processes for different Memristive PUFs designs, and make a comparison between these results. Finally, concluded that most of the circuits are evaluated by simulation, whereas few other circuits were evaluated by fabrication owing to the expensive fabrication process. Since the memristor is a prototype and not commercialized yet, it is expected to be adopted and marketed in the next generation of hardware security.

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Section: Implementation Of Memristive Puf (M-puf)mentioning

confidence: 99%

Memristive Physical Unclonable Functions: The State-of-the-Art Technology

Al-Khaboori,

Al-Mashhadani

2023

IJSSE

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“…These parameters can be detected only when they are needed to produce the unique responses. These variables include the time, the frequency, the current or the voltage, the bistable states and the capacitance [24]. The second category of PUFs, referred to as non-electronic PUFs, includes the PUF elements that rely on unique characteristics of the physical system in a non-electronic manner, such as the use of light in optical PUFs [25] and the radio variations in the RF-PUF [26].…”

Section: Physical Unclonable Functionmentioning

confidence: 99%

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review

Khalfaoui

Leneutre

Villard³

et al. 2021

Sensors

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The demand for Internet of Things services is increasing exponentially, and consequently a large number of devices are being deployed. To efficiently authenticate these objects, the use of physical unclonable functions (PUFs) has been introduced as a promising solution for the resource-constrained nature of these devices. The use of machine learning PUF models has been recently proposed to authenticate the IoT objects while reducing the storage space requirement for each device. Nonetheless, the use of a mathematically clonable PUFs requires careful design of the enrollment process. Furthermore, the secrecy of the machine learning models used for PUFs and the scenario of leakage of sensitive information to an adversary due to an insider threat within the organization have not been discussed. In this paper, we review the state-of-the-art model-based PUF enrollment protocols. We identity two architectures of enrollment protocols based on the participating entities and the building blocks that are relevant to the security of the authentication procedure. In addition, we discuss their respective weaknesses with respect to insider and outsider threats. Our work serves as a comprehensive overview of the ML PUF-based methods and provides design guidelines for future enrollment protocol designers.

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Survey on the benefits of using memristors for PUFs

Cited by 2 publications

References 89 publications

Memristive Physical Unclonable Functions: The State-of-the-Art Technology

Memristive Physical Unclonable Functions: The State-of-the-Art Technology

Security Analysis of Machine Learning-Based PUF Enrollment Protocols: A Review

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